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dc.contributor.authorKrause, Rüdigeren
dc.contributor.authorTutz, Gerharden
dc.date.accessioned2006-10-16-
dc.date.accessioned2010-05-14T10:14:46Z-
dc.date.available2010-05-14T10:14:46Z-
dc.date.issued2004-
dc.identifier.pidoi:10.5282/ubm/epub.1760en
dc.identifier.piurn:nbn:de:bvb:19-epub-1760-6en
dc.identifier.urihttp://hdl.handle.net/10419/31159-
dc.description.abstractGene expression datasets usually have thousends of explanatory variables which are observed on only few samples. Generally most variables of a dataset have no effect and one is interested in eliminating these irrelevant variables. In order to obtain a subset of relevant variables an appropriate selection procedure is necessary. In this paper we propose the selection of variables by use of genetic algorithms with the logistic regression as underlying modelling procedure. The selection procedure aims at minimizing information criteria like AIC or BIC. It is demonstrated that selection of variables by genetic algorithms yields models which compete well with the best available classification procedures in terms of test misclassification error.en
dc.language.isoengen
dc.publisher|aLudwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen |cMünchenen
dc.relation.ispartofseries|aDiscussion Paper |x390en
dc.subject.ddc519en
dc.subject.keywordGenetic algorithmen
dc.subject.keywordVariable selectionen
dc.subject.keywordLogistic regressionen
dc.subject.keywordAICen
dc.subject.keywordBICen
dc.titleVariable selection and discrimination in gene expression data by genetic algorithms-
dc.type|aWorking Paperen
dc.identifier.ppn518805573en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen

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